As aforementioned, there are a loss deviation term (DEV) and a loss stabilization term (STA) in the objective function. The loss deviation term follows [1]} to enlarge the difference between CNN predictions and the ground truth label. We denote the input image as \(x\) , model parameters as \(\theta \) , and the ground truth label of \(x\)  as \(Y\) . In the first iteration, the objective function of SMIA denoted as \(\mathcal {L}_{\rm SMIA}\)  can be written as:
\(\mathcal {L}_{\rm SMIA} = \mathcal {L}_{\rm DEV}=\mathcal {L}(f(\theta , x), Y),\) 
